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Decision trees are a popular algorithm for classification and regression tasks from "summary" of Data Science for Business by Foster Provost,Tom Fawcett
Decision trees are widely used in data science for both classification and regression tasks due to their simplicity and interpretability. They are essentially flowcharts that help guide decisions based on input features. A decision tree starts at the root node and splits the data into subsets based on the values of certain features. This process continues recursively until a stopping criterion is met, such as reaching a maximum tree depth or having data points that belong to the same class. One of the main reasons decision trees are popular is their simplicity. They are easy to understand and interpret, even for non-experts in data science. A decision tree can be visualized as a tree structure, with branches representing decisions and leaves representing outcomes. This makes it easy to explain the reasoning behind a particular pred...
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